A systematic approach to identifying key parameters and processes in agroecosystem models

Vasileios Myrgiotis, Robert M. Rees, Cairistiona F.E. Topp and Mathew Williams

Ecological Modelling, 2018, vol. 368, issue C, 344-356

Abstract: Process-based agroecosystem biogeochemistry models are widely used to quantify the flow of water and nutrients in agricultural ecosystems and they have become important tools in the effort to address the twin challenges of reducing greenhouse gas emissions and improving agricultural sustainability. Model parameters require careful calibration, as they affect the simulated processes and outputs. Sensitivity analysis (SA) is commonly used to quantify the impacts of parameters on outputs, and guide the calibration process. Here we demonstrate a systematic approach for SA, which assures that (1) the role of time-dependency in the sensitivity indices is considered and (2) the SA is not biased by the edapho-climatic conditions at individual sites. Demonstrating this approach, we examine the parametric sensitivity of an advanced agroecosystem model (Landscape-DNDC) using a framework that is based on (1) the Sobol SA method, (2) model simulations at three UK arable sites and (3) the grouping of the model's parameters according to the processes they affect. The findings of this research identify the parameters and processes that should be carefully examined in order to minimise the impact of parametric uncertainty on model outputs. We show that a limited number of parameters are responsible for a large part of the sensitivity of model outputs. The description of soil microbial dynamics is identified as a key source of output sensitivity. Also, we show that individual management activities can significantly affect the time-dependency of the parametric sensitivity indices for certain model outputs.

Keywords: Soil biogeochemistry; Ecosystem modelling; Landscape-DNDC; Sensitivity analysis (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:368:y:2018:i:c:p:344-356

DOI: 10.1016/j.ecolmodel.2017.12.009

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